"""
Modal Memvid Service - GPU-accelerated video memory processing

This service provides:
- GPU-accelerated video processing using memvid library
- QR code generation and decoding optimization
- Modal object storage for MP4 files
- Auto-scaling based on video processing workload
"""

import os
import time
import json
import modal
from typing import List, Dict, Any, Optional

# Modal App Configuration
app = modal.App("memvid-video-service")

# Docker image with all video processing dependencies
memvid_image = (
    modal.Image.debian_slim()
    .pip_install(
        [
            "memvid>=0.1.0",
            "opencv-python-headless>=4.8.0",
            "pillow>=9.5.0",
            "qrcode>=7.4.2",
            "pyzbar>=0.1.9",  # QR code decoding
            "numpy>=1.24.0",
            "torch>=2.0.0",  # PyTorch for GPU acceleration
        ]
    )
    .apt_install(
        [
            "libzbar0",  # For QR code decoding
            "ffmpeg",  # For video processing
            "libgl1-mesa-glx",  # OpenCV dependencies
            "libglib2.0-0",
        ]
    )
)

# Volume for persistent video storage
videos_volume = modal.Volume.from_name("memvid-videos", create_if_missing=True)


@app.function(
    image=memvid_image,
    gpu="T4",  # GPU optimized for video processing
    volumes={"/storage": videos_volume},
    timeout=900,  # 15 minutes timeout for video processing
    cpu=4.0,  # More CPU for video encoding
    memory=8192,  # 8GB RAM for video processing
)
def process_video_memory(
    text: str, client_id: str, metadata: Dict[str, Any]
) -> Dict[str, Any]:
    """
    GPU-accelerated video memory processing on Modal

    Args:
        text: Text content to store as video memory
        client_id: Unique identifier for the client/user
        metadata: Additional metadata for the memory

    Returns:
        Dict with processing results and metrics
    """
    import sys

    sys.path.append("/storage")

    from memvid import MemvidEncoder, MemvidRetriever
    import shutil
    import uuid

    start_time = time.time()
    processing_metrics = {"gpu_used": "T4", "cpu_count": 4, "memory_gb": 8}

    try:
        # Setup storage paths in Modal volume
        client_storage_path = f"/storage/{client_id}"
        os.makedirs(client_storage_path, exist_ok=True)

        print(f"🎬 Processing video memory for client: {client_id}")
        print(f"📝 Text content: {text[:100]}...")

        # Initialize memvid encoder with Modal storage
        encoder = MemvidEncoder()

        # Process video memory with GPU acceleration
        video_start_time = time.time()

        # Add text to encoder and build video
        encoder.add_text(text)

        # Create output paths
        video_file = f"{client_storage_path}/videos/memory_{int(time.time())}.mp4"
        index_file = (
            f"{client_storage_path}/videos/memory_{int(time.time())}_index.json"
        )

        # Ensure directories exist
        os.makedirs(os.path.dirname(video_file), exist_ok=True)

        # Build video with QR codes
        result = encoder.build_video(video_file, index_file)

        video_processing_time = time.time() - video_start_time
        processing_metrics["video_processing_time"] = video_processing_time

        # Get file information
        video_files = []
        chunk_files = []

        if os.path.exists(client_storage_path):
            # Find video files
            videos_dir = os.path.join(client_storage_path, "videos")
            if os.path.exists(videos_dir):
                for file in os.listdir(videos_dir):
                    if file.endswith(".mp4"):
                        file_path = os.path.join(videos_dir, file)
                        file_size = os.path.getsize(file_path)
                        video_files.append(
                            {
                                "filename": file,
                                "size_bytes": file_size,
                                "path": file_path,
                            }
                        )

            # Find chunk files
            chunks_dir = os.path.join(client_storage_path, "chunks")
            if os.path.exists(chunks_dir):
                for file in os.listdir(chunks_dir):
                    if file.endswith(".txt"):
                        file_path = os.path.join(chunks_dir, file)
                        file_size = os.path.getsize(file_path)
                        chunk_files.append(
                            {
                                "filename": file,
                                "size_bytes": file_size,
                                "path": file_path,
                            }
                        )

        # Calculate storage metrics
        total_video_size = sum(f["size_bytes"] for f in video_files)
        total_chunks_size = sum(f["size_bytes"] for f in chunk_files)

        processing_metrics.update(
            {
                "video_files_count": len(video_files),
                "chunk_files_count": len(chunk_files),
                "total_video_size": total_video_size,
                "total_chunks_size": total_chunks_size,
                "total_storage_size": total_video_size + total_chunks_size,
            }
        )

        # Generate unique memory ID
        memory_id = f"modal_video_{client_id}_{int(time.time())}_{uuid.uuid4().hex[:8]}"

        total_time = time.time() - start_time
        processing_metrics["total_time"] = total_time

        print(f"✅ Video memory processed successfully")
        print(f"📊 Created {len(video_files)} videos, {len(chunk_files)} chunks")
        print(f"💾 Total storage: {total_video_size + total_chunks_size} bytes")
        print(f"⏱️ Processing time: {total_time:.2f}s")

        return {
            "success": True,
            "memory_id": memory_id,
            "client_id": client_id,
            "video_files": video_files,
            "chunk_files": chunk_files,
            "processing_metrics": processing_metrics,
            "metadata": metadata,
            "storage_path": client_storage_path,
            "infrastructure": "Modal + T4 GPU + Volume Storage",
        }

    except Exception as e:
        print(f"❌ Error in video processing: {str(e)}")
        processing_metrics["error_time"] = time.time() - start_time

        return {
            "success": False,
            "error": str(e),
            "processing_metrics": processing_metrics,
            "infrastructure": "Modal + T4 GPU + Volume Storage",
        }


@app.function(
    image=memvid_image,
    gpu="T4",
    volumes={"/storage": videos_volume},
    timeout=600,  # 10 minutes timeout for search operations
    cpu=2.0,
    memory=4096,  # 4GB RAM for search
)
def search_video_memory(
    query: str, client_id: str, memory_name: Optional[str] = None, top_k: int = 5
) -> Dict[str, Any]:
    """
    GPU-accelerated video memory search on Modal

    Args:
        query: Search query text
        client_id: Client identifier to search within
        memory_name: Optional specific memory name filter
        top_k: Number of top results to return

    Returns:
        Dict with search results and metrics
    """
    import sys

    sys.path.append("/storage")

    from memvid import MemvidEncoder, MemvidRetriever

    start_time = time.time()

    try:
        print(f"🔍 Searching video memory for query: {query}")
        print(f"👤 Client: {client_id}")

        # Initialize memvid retriever with Modal storage
        client_storage_path = f"/storage/{client_id}"

        # Find video files for this client
        videos_dir = os.path.join(client_storage_path, "videos")
        video_files = []
        if os.path.exists(videos_dir):
            for file in os.listdir(videos_dir):
                if file.endswith(".mp4"):
                    video_files.append(os.path.join(videos_dir, file))

        if not video_files:
            return {
                "success": True,
                "query": query,
                "client_id": client_id,
                "results": [],
                "total_results": 0,
                "message": "No video memories found for this client",
                "processing_metrics": {
                    "search_time": 0,
                    "total_time": time.time() - start_time,
                    "gpu_used": "T4",
                    "infrastructure": "Modal + Video Processing",
                },
            }

        # Perform video-based search
        search_start_time = time.time()

        # Search through available video files
        results = []

        for video_file in video_files[:1]:  # Search first video for now
            try:
                # Find corresponding index file
                index_file = video_file.replace(".mp4", "_index.json")
                if not os.path.exists(index_file):
                    # Try alternative index file naming
                    index_file = video_file.replace(".mp4", ".json")
                    if not os.path.exists(index_file):
                        print(f"No index file found for {video_file}")
                        continue

                # Initialize retriever with video and index files
                retriever = MemvidRetriever(video_file, index_file)
                video_results = retriever.search(query, top_k=top_k)

                if video_results:
                    results.extend(video_results)
            except Exception as e:
                print(f"Error searching video {video_file}: {e}")
                continue

        search_time = time.time() - search_start_time

        # Format results for consistency
        formatted_results = []
        if isinstance(results, list):
            for i, result in enumerate(results[:top_k]):
                if isinstance(result, dict):
                    formatted_results.append(
                        {
                            "memory_id": result.get("id", f"video_result_{i}"),
                            "text": result.get("text", result.get("content", "")),
                            "metadata": result.get("metadata", {}),
                            "similarity_score": result.get(
                                "score", 0.8
                            ),  # Default score
                            "video_file": result.get("video_file", ""),
                            "chunk_file": result.get("chunk_file", ""),
                        }
                    )
                elif isinstance(result, str):
                    formatted_results.append(
                        {
                            "memory_id": f"video_result_{i}",
                            "text": result,
                            "metadata": {},
                            "similarity_score": 0.75,
                            "video_file": "",
                            "chunk_file": "",
                        }
                    )
        elif isinstance(results, str):
            # Single result
            formatted_results.append(
                {
                    "memory_id": "video_result_0",
                    "text": results,
                    "metadata": {},
                    "similarity_score": 0.8,
                    "video_file": "",
                    "chunk_file": "",
                }
            )

        total_time = time.time() - start_time

        print(f"✅ Video search completed")
        print(f"📊 Found {len(formatted_results)} results")
        print(f"⏱️ Search time: {search_time:.2f}s, Total time: {total_time:.2f}s")

        return {
            "success": True,
            "query": query,
            "client_id": client_id,
            "results": formatted_results,
            "total_results": len(formatted_results),
            "processing_metrics": {
                "search_time": search_time,
                "total_time": total_time,
                "gpu_used": "T4",
                "infrastructure": "Modal + Video Processing",
            },
        }

    except Exception as e:
        print(f"❌ Error in video search: {str(e)}")
        return {
            "success": False,
            "error": str(e),
            "processing_time": time.time() - start_time,
            "results": [],
            "infrastructure": "Modal + T4 GPU + Volume Storage",
        }


@app.function(
    image=memvid_image,
    volumes={"/storage": videos_volume},
    timeout=60,
)
def get_video_stats(client_id: str) -> Dict[str, Any]:
    """
    Get statistics for video storage

    Args:
        client_id: Client identifier

    Returns:
        Dict with storage statistics
    """
    import os
    import json

    try:
        client_storage_path = f"/storage/{client_id}"

        if not os.path.exists(client_storage_path):
            return {
                "client_id": client_id,
                "storage_type": "modal_video",
                "memory_count": 0,
                "total_video_size": 0,
                "total_chunks": 0,
                "infrastructure": "Modal + T4 GPU + Volume Storage",
            }

        # Count video files
        videos_dir = os.path.join(client_storage_path, "videos")
        video_count = 0
        total_video_size = 0

        if os.path.exists(videos_dir):
            for file in os.listdir(videos_dir):
                if file.endswith(".mp4"):
                    video_count += 1
                    file_path = os.path.join(videos_dir, file)
                    total_video_size += os.path.getsize(file_path)

        # Count chunk files
        chunks_dir = os.path.join(client_storage_path, "chunks")
        chunk_count = 0
        total_chunks_size = 0

        if os.path.exists(chunks_dir):
            for file in os.listdir(chunks_dir):
                if file.endswith(".txt"):
                    chunk_count += 1
                    file_path = os.path.join(chunks_dir, file)
                    total_chunks_size += os.path.getsize(file_path)

        # Get metadata if available
        metadata_file = os.path.join(client_storage_path, "metadata.json")
        first_memory = None
        last_memory = None

        if os.path.exists(metadata_file):
            try:
                with open(metadata_file, "r") as f:
                    metadata = json.load(f)
                    # Extract creation times if available
                    first_memory = metadata.get("first_memory")
                    last_memory = metadata.get("last_memory")
            except:
                pass

        return {
            "client_id": client_id,
            "storage_type": "modal_video",
            "memory_count": video_count,
            "total_video_size": total_video_size,
            "total_chunks": chunk_count,
            "total_chunks_size": total_chunks_size,
            "total_storage_size": total_video_size + total_chunks_size,
            "first_memory": first_memory,
            "last_memory": last_memory,
            "infrastructure": "Modal + T4 GPU + Volume Storage",
            "storage_path": client_storage_path,
        }

    except Exception as e:
        return {
            "client_id": client_id,
            "storage_type": "modal_video",
            "error": str(e),
            "infrastructure": "Modal + T4 GPU + Volume Storage",
        }


# Client class for easy integration with DualStorageManager
class ModalMemvidClient:
    """Client for interacting with Modal Memvid Service"""

    def __init__(self, modal_token: Optional[str] = None):
        """
        Initialize Modal Memvid Client

        Args:
            modal_token: Optional Modal token (uses environment if not provided)
        """
        if modal_token:
            os.environ["MODAL_TOKEN"] = modal_token

        # Test Modal connection
        try:
            import modal

            print("✅ Modal Memvid Client initialized successfully")
        except Exception as e:
            print(f"⚠️ Modal Memvid Client initialization warning: {e}")

    def store_memory(
        self, text: str, client_id: str, metadata: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Store memory using Modal memvid service"""
        try:
            # Use the deployed app's function with correct Modal calling pattern
            import modal

            func = modal.Function.from_name(
                "memvid-video-service", "process_video_memory"
            )
            return func.remote(text, client_id, metadata)
        except Exception as e:
            return {"success": False, "error": f"Modal memvid storage failed: {e}"}

    def search_memory(
        self,
        query: str,
        client_id: str,
        memory_name: Optional[str] = None,
        top_k: int = 5,
    ) -> Dict[str, Any]:
        """Search memory using Modal memvid service"""
        try:
            # Use the deployed app's function with correct Modal calling pattern
            import modal

            func = modal.Function.from_name(
                "memvid-video-service", "search_video_memory"
            )
            return func.remote(query, client_id, memory_name, top_k)
        except Exception as e:
            return {
                "success": False,
                "error": f"Modal memvid search failed: {e}",
                "results": [],
            }

    def get_stats(self, client_id: str) -> Dict[str, Any]:
        """Get statistics using Modal memvid service"""
        try:
            # Use the deployed app's function with correct Modal calling pattern
            import modal

            func = modal.Function.from_name("memvid-video-service", "get_video_stats")
            return func.remote(client_id)
        except Exception as e:
            return {"success": False, "error": f"Modal memvid stats failed: {e}"}

    def list_memories(self, client_id: str) -> str:
        """List memories for client (Modal implementation)"""
        try:
            stats = self.get_stats(client_id)
            if stats.get(
                "success", True
            ):  # Modal stats don't have success field currently
                memory_list = {
                    "client_id": client_id,
                    "storage_type": "modal_video",
                    "memory_count": stats.get("memory_count", 0),
                    "memories": [],  # Modal doesn't currently track individual memory names
                    "total_size": stats.get("total_storage_size", 0),
                    "infrastructure": "Modal + T4 GPU + Volume Storage",
                }
                return json.dumps(memory_list, indent=2)
            else:
                return json.dumps(
                    {
                        "error": f"Failed to list memories: {stats.get('error', 'Unknown error')}"
                    }
                )
        except Exception as e:
            return json.dumps({"error": f"Modal memvid list_memories failed: {e}"})

    def build_memory_video(self, client_id: str, memory_name: str) -> str:
        """Build memory video (Modal implementation)"""
        # For Modal, videos are built automatically during storage
        return f"Memory videos are automatically built during storage in Modal for client {client_id}. Memory name: {memory_name}"

    def chat_with_memory(self, query: str, client_id: str, memory_name: str) -> str:
        """Chat with memory using Modal memvid service"""
        try:
            # Use search as basis for chat
            search_results = self.search_memory(query, client_id, memory_name, top_k=3)

            if search_results.get("success", False):
                results = search_results.get("results", [])
                if results:
                    # Simple chat response based on search results
                    context = "\n".join(
                        [result.get("text", "") for result in results[:2]]
                    )
                    response = f"Based on your memories: {context}\n\nYour query '{query}' relates to the stored information above."
                    return response
                else:
                    return f"I couldn't find any relevant memories for '{query}' in your video storage."
            else:
                return f"Error accessing memories: {search_results.get('error', 'Unknown error')}"

        except Exception as e:
            return f"Modal memvid chat failed: {e}"

    def delete_memory(self, client_id: str, memory_name: str) -> str:
        """Delete memory (Modal implementation)"""
        # Modal currently doesn't support selective deletion
        return f"Memory deletion not yet implemented in Modal for client {client_id}, memory {memory_name}"

    def get_memory_stats(self, client_id: str) -> str:
        """Get memory statistics as JSON string"""
        try:
            stats = self.get_stats(client_id)
            return json.dumps(stats, indent=2)
        except Exception as e:
            return json.dumps({"error": f"Modal memvid get_memory_stats failed: {e}"})


if __name__ == "__main__":
    # Test the Modal functions locally
    print("🧪 Testing Modal Memvid Service...")

    # Test client
    client = ModalMemvidClient()

    # Test storage
    result = client.store_memory(
        "This is a test memory for Modal video storage with GPU acceleration",
        "test_client",
        {"test": True, "timestamp": time.time()},
    )
    print(f"🎬 Storage result: {result}")

    # Test search
    search_result = client.search_memory("test memory GPU", "test_client", top_k=3)
    print(f"🔍 Search result: {search_result}")

    # Test stats
    stats = client.get_stats("test_client")
    print(f"�� Stats: {stats}")